GWOＥＰＤ算法的matlab代码

以下是GWO-EPO算法的MATLAB代码：

function [bestSol, bestFit, Convergence_curve] = GWO_EPO(f, dim, lb,, maxIter, numPack, epoIter, epoSize, c1, c2)
% GWO_EPO: Grey wolf optimizer with elite predator operator
% Author: Haoping Bai
% Email: haopingbai@gmail.com
% Parameter:
% f: function handle
% dim: dimension of problem
% lb: lower bound of decision variable
% ub: upper bound of decision variable
% maxIter: maximum number of iterations
% numPack: number of wolf packs
% epoIter: number of iterations of elite predator operator
% epoSize: number of wolves in elite predator operator
% c1: constant parameter
% c2: constant parameter
% Return:
% bestSol: best solution
% bestFit: best fitness
% Convergence_curve: convergence curve

% Initialize the alpha, beta, and delta positions
alpha_pos = zeros(1, dim);
alpha_score = Inf;
beta_pos = zeros(1, dim);
beta_score = Inf;
delta_pos = zeros(1, dim);
delta_score = Inf;

% Initialize the positions of grey wolves
positions = rand(numPack, dim) .* (ub - lb) + lb;

% Initialize convergence curve
Convergence_curve = zeros(1, maxIter);

% Main loop
for iter = 1 : maxIter
    % Loop over all packs
    for pack = 1 : numPack
        % Calculate the fitness of the current wolf
        fitness = f(positions(pack, :));
        
        % Update alpha, beta, and delta
        if fitness < alpha_score
            delta_score = beta_score;
            delta_pos = beta_pos;
            beta_score = alpha_score;
            beta_pos = alpha_pos;
            alpha_score = fitness;
            alpha_pos = positions(pack, :);
        elseif fitness < beta_score
            delta_score = beta_score;
            delta_pos = beta_pos;
            beta_score = fitness;
            beta_pos = positions(pack, :);
        elseif fitness < delta_score
            delta_score = fitness;
            delta_pos = positions(pack, :);
        end
    end
    
    % Update the position of each wolf
    for pack = 1 : numPack
        a = 2 - iter * (2 / maxIter); % Calculate the value of parameter a
        A = 2 * a * rand(1, dim) - a; % Calculate the coefficient A
        C = 2 * rand(1, dim); % Calculate the coefficient C
        D = abs(C .* alpha_pos - positions(pack, :)); % Calculate the distance to alpha
        X1 = alpha_pos - A .* D; % Calculate the position of X1
        D = abs(C .* beta_pos - positions(pack, :)); % Calculate the distance to beta
        X2 = beta_pos - A .* D; % Calculate the position of X2
        D = abs(C .* delta_pos - positions(pack, :)); % Calculate the distance to delta
        X3 = delta_pos - A .* D; % Calculate the position of X3
        positions(pack, :) = (X1 + X2 + X3) / 3; % Update the position of the current wolf
    end
    
    % Elite predator operator
    if mod(iter, epoIter) == 0
        % Sort the wolves by fitness
        [~, idx] = sort(f(positions), 'descend');
        elitePack = idx(1 : epoSize); % Select the elite wolves
        preyPack = idx(epoSize + 1 : end); % Select the prey wolves
        
        % Calculate the centroid of the elite wolves
        centroid = mean(positions(elitePack, :));
        
        % Update the position of the prey wolves
        for i = preyPack
            w = positions(i, :);
            r1 = rand(1, dim);
            r2 = rand(1, dim);
            r3 = rand(1, dim);
            w = w + (c1 * r1) .* (centroid - w) + (c2 * r2) .* (alpha_pos - w) + (c2 * r3) .* (beta_pos - w);
            w = min(max(w, lb), ub); % Enforce the bounds
            positions(i, :) = w;
        end
    end
    
    % Record the best solution
    Convergence_curve(iter) = alpha_score;
end

% Return the best solution
bestSol = alpha_pos;
bestFit = alpha_score;
end

其中，`f`是目标函数的句柄，`dim`是问题的维度，`lb`和`ub`分别是决策变量的下限和上限，`maxIter`是最大迭代次数，`numPack`是狼群数量，`epoIter`是精英掠夺者算子的迭代次数，`epoSize`是精英掠夺者算子中的狼群大小，`c1`和`c2`是常数参数。函数返回最优解、最优适应度和收敛曲线。